Surveying apparatus and method of analyzing measuring data

ABSTRACT

Embodiments of the invention relate to a method for minimizing the influence of disturbing signals during calculation of shape elements from coordinate points. An aim of the embodiments of the invention is to exclude the coordinates which are not to be locally assigned to the desired shaped element from the calculation of the shaped element. Said aim is achieved by combining compensation methods for calculating the desired type of shaped element with recognition methods for the same type of shaped element and using the recognition methods for filtering the coordinate points that are relevant for calculating the shaped element out of all input coordinate points.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/078,637 filed Mar. 11, 2005, now U.S. Pat. No. 7,246, 034, which is acontinuation-in-part of International Application No. PCT/EP2003/009996,with an international filing date of Sep. 9, 2003, which InternationalApplication was published by the International Bureau on Apr. 1, 2004,and which was not published in English, the entire contents of each ofthese applications are incorporated herein by reference. Thisapplication also claims the benefit of DE 102 42 852.2 filed on Sep. 14,2002, the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to surveying instruments and methods ofanalyzing measuring data.

2. Brief Description of Related Art

A conventional surveying apparatus, such as a total-station, atacheometer, and a theodolite, comprise an optical system, such as atelescope, having a predetermined optical axis carried on a mountingstructure such as a tripod. A user, while looking through the telescope,adjusts an orientation of the telescope relative to the tripod such thata target of interest coincides with the optical axis of the telescope.The target of interest may comprise a reflector or pole or otherfeatures of an object such as an edge of a building or a landmark orothers. By measuring the angle of the telescope relative to the mountingstructure, it is possible to determine a position of the target. Forthis purpose, the surveying apparatus may comprise angle sensors, anoptical distance measuring system and a light projection systemtransmitting a light beam through the telescope to a reflector locatedat the target.

Recently, research has been performed to develop a surveying apparatuscomprising a camera for detecting an image of a scenario including thetarget, wherein an image processing is performed to automaticallyidentify the target within the image and to determine the position ofthe target within the image.

It has been found that the conventional methods of image processing maynot always achieve a satisfactory measuring result. The images detectedby the camera are subject to disturbances due to varying lightingconditions. Moreover, the object to be identified in the image may havecomplex structures due to textured surfaces and image backgrounds, andportions of the objects to be identified may be hidden by other objects,such as trees. Therefore, a reliable and robust detection of objectfeatures is not always possible.

SUMMARY OF THE INVENTION

The present invention has been accomplished taking the above problemsinto consideration.

Embodiments of the present invention provide a surveying apparatusallowing improved detection of features of an object in an imagerecorded by a camera.

Embodiments of the present invention provide a method of analyzingmeasuring data representing features of an object.

Embodiments of the present invention provide a method of analyzingmeasuring data comprising: inputting measuring data from a measuringdevice, the measuring data comprising a set of data values representingfeatures of an object; performing a recognition method on at least asubset of the data values for determining first parameters of ageometric shape element representing at least a portion of a feature ofthe object; eliminating outliers from the set of data values, theoutliers having a distance from the shape element having the determinedfirst parameters greater than a first threshold; and performing a firstregression analysis on a set of remaining data values not including theoutliers, for determining second parameters of the geometric shapeelement such that the shape element having the second parameters is abetter representation of the portion of the at least one feature of theobject than the shape element having the first parameters.

The inventors have found that measuring data including disturbances arenot always well-suited to be processed by a regression analysis fordetermining parameters of a geometric shape element such that the shapeelement best approximates the feature of the object. In particular,outliers included in the measuring data tend to deteriorate the resultof the regression analysis.

According to embodiments of the present invention, a recognition methodis applied to the measuring data for identifying a geometric shapeelement represented by the disturbed measuring data. Thereafter,outliers of the measuring data and representing disturbances of themeasuring data are eliminated, and the first regression analysis isperformed on the remaining measuring data not including the outliers. Aresult of the regression analysis is a set of parameters of thegeometric shape element coinciding with the desired feature of theobject to a high accuracy. It has been found that the recognition methodapplied to the disturbed measuring data is well-suited for identifyingthe outliers such that the remaining measuring data not including theoutliers form an improved basis for determining the parameters of theshape element by regression analysis.

According to an exemplary embodiment of the invention, the recognitionmethod comprises determining the first parameters of the geometric shapeelement such that a number of data values coinciding with the geometricshape element having the first parameters has a maximum value. Suchrecognition method has been found to be very robust even if asignificant amount of outliers is included in the measuring data, andthe outliers may be easily identified and eliminated thereafter.

According to an exemplary embodiment, the recognition method comprisesperforming a Hough transformation based on the disturbed measuring data.Background information relating to the Hough transformation is disclosedin U.S. Pat. No. 3,069,654 and in “Praxis der digitalen Bildverarbeitungund Mustererkennung” by P. Haberäcker, Munich, Hanser, 1995, pages 294to 308. The entire contents of these documents are incorporated hereinby reference.

According to an exemplary embodiment of the invention, the eliminationof the outliers from the set of data values is based on an analysis of adistance of the data values from the shape element having the firstparameters determined in the recognition method. Data values having adistance from the shape element which is greater than a first thresholdare identified as outliers. According to an embodiment herein, thedistance is calculated as the Euclidian distance of the respective datavalue from the shape element having the first parameters determined inthe recognition method.

The first threshold may be a predetermined threshold or a thresholddetermined in dependence of an analysis of a distribution of values ofthe distances of the data values from the shape element having thedetermined first parameters.

The geometric shape element may comprise a straight-line, a circle, anellipse, a cylinder or other suitable shapes.

According to an exemplary embodiment, the shape element is astraight-line, and the first parameters may comprise a slope and anoffset of the straight-line or any other suitable representation of thestraight-line.

According to an exemplary embodiment of the invention, the recognitionmethod is preceded by a second regression analysis for determiningparameters of the geometric shape element. Thereafter, a quality of anapproximation of the feature of the object by the geometric shapeelement having the parameters determined in the second regressionanalysis is determined and the recognition method is only performed if avalue representing the quality of the representation indicates that thequality is insufficient. With such method it is possible to avoid therecognition method requiring extensive calculations in situations wheredisturbances of the measuring data are relatively low.

According to an exemplary embodiment herein, the determination of thequality of the representation of the feature of the object by thegeometric shape element having the parameters determined in the secondregression analysis is based on distances of the data values from thecorresponding shape element. Again, the distances may be Euclideandistances of the data values from the shape element.

The quality of the representation may be found to be sufficient if avalue representing the quality does not exceed a suitable secondthreshold. The second threshold may be a predetermined threshold or athreshold determined according to an analysis of a distribution ofvalues of the distances of the data values from the shape element havingthe third parameters determined in the second regression analysis.

According to a further aspect of the present invention, there isprovided a surveying apparatus comprising a camera and a controller forreceiving image data from the camera. The controller is configured togenerate measuring data to be analyzed from the image data, and toperform the above illustrated method of analyzing the generatedmeasuring data.

It has been found that the above illustrated method is a robust methodfor identifying geometric shape elements representing a feature of anobject imaged by the camera also in situations where significantdisturbances are included in the image data.

For example, the surveying apparatus may be used to measure a positionof an edge of a building imaged by the camera. Portions of the edge ofthe building may be hidden by obstacles, such as a tree. A suitableshape element for approximating the edge of the building is astraight-line. The method implemented in the controller determinesparameters of the straight-line such that the straight-line coincideswith the representation of the edge of the building in the detectedimage. Such determination of the parameters of the straight-line isrobust and largely not affected by the obstacles hiding portions of theedge of the building in the image or by other disturbances.

According to an exemplary embodiment of the invention, the surveyingapparatus further comprises an input device for receiving a userselection of a type of the geometric shape element. The geometric shapeelements may comprise elements such as a straight-line, a circle, anellipse and a cylinder.

According to a further exemplary embodiment, the surveying apparatusfurther comprises a display for displaying an image represented by theimage data. The apparatus may further comprise an input device forselecting a portion of the displayed image. The controller is thenfurther configured to generate the measuring data subject to theanalysis according to the above illustrated method from the image datarepresenting the selected portion of the displayed image. A user mayperform a short analysis of the displayed image and determine a portionof the image containing the target of interest of the surveying task,such as an edge of a building. By selecting the portion containing thetarget, it is possible to reduce an amount of data to be processed bythe analyzing method.

According to an exemplary embodiment of the present invention, thesurveying apparatus comprises an output device for receiving thedetermined parameters of the shape element. The output device maycomprise the display or a memory, such as a magnetic memory or asolid-state memory which may be included in the controller.

A computer-readable carrier containing information representing acomputer program adapted to cause a processing unit of a controller toexecute the methods described herein is further provided. Thecomputer-readable carrier can be any suitable type of carrier such as asolid-state memory, a magnetic memory, optical memory; other type ofmemory, or modulated waves/signals (e.g. radio frequency, audiofrequency, or optical frequency modulated waves/signals) suitable forbeing transmitted through any suitable network, such as the internet.

A computer system comprising a processor and a program storage devicereadable by the computer system, tangibly embodying a program ofinstructions executable by the processor to perform the methodsdescribed herein is further provided. The computer system may beseparate from the surveying apparatus illustrated above. For example, asurveying apparatus may record image data from a camera on a storagedevice, such as a solid-state memory, a hard disc or other suitablestorage devices, and the image data may be transferred to the computersystem for further analysis according to the methods described herein.For example, it is possible to transfer the image data from thesurveying apparatus to the computer system through a suitable network,such as a wireless network or the internet.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing as well as other advantageous features of the inventionwill be more apparent from the following detailed description ofexemplary embodiments of the invention with reference to theaccompanying drawings. It is noted that not all possible embodiments ofthe present invention necessarily exhibit each and every, or any, of theadvantages identified herein.

FIG. 1 is a schematic illustration of a surveying apparatus according toan embodiment of the invention and having the method according to anembodiment of the invention implemented therein;

FIG. 2 is a graph showing a set of data values obtained from a measuringdevice and including disturbances;

FIG. 3 is a graph showing the set of data values of FIG. 2 and includinga line determined by a regression analysis fit;

FIG. 4 is a graph showing the measuring data of FIG. 1 and including astraight-line determined by a regression analysis fit based on the datavalues not including outliers;

FIG. 5 is a flowchart illustrating a method of analyzing measuring dataaccording to a first embodiment of the invention;

FIG. 6 is an illustration representing details of the method illustratedin FIG. 5;

FIG. 7 is a representation of a Hough parameter space exemplified by theshape element “straight-line”;

FIG. 8 is a flowchart illustrating a method of analyzing measuring dataaccording to a second embodiment of the invention; and

FIG. 9 is an illustration of a computer system according to anembodiment of the invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

In the exemplary embodiments described below, components that are alikein function and structure are designated as far as possible by alikereference numerals. Therefore, to understand the features of theindividual components of a specific embodiment, the descriptions ofother embodiments and of the summary of the invention should be referredto.

The invention includes methods of filtering coordinate points in thecalculation of shape elements with the aim to exclude such coordinatesfrom the shape element calculation which can locally not be assigned tothe expected ideal geometric shape.

The calculation of the shape of the object of measurement from a set ofmeasuring data, such as coordinate points of the object surface thathave been acquired by probing, is a well-known problem in coordinatemeasurement. The shape is described by ideal geometric substituteelements (briefly called shape elements), such as straight line, circle,ellipse, sphere, or cylinder. The acquired coordinate points are partlyin random order and, due to disturbing influences, deviate from the realgeometry of the object. In particular in situations where coordinatepoints are optically acquired by means of image sensors, such as acamera, the deviations from the real shape may be substantial. This maybe caused, for example, by instable lighting conditions, dust depositson the object edges, or textured surfaces and image backgrounds. If theunfiltered coordinate points are used in a regression analysis fordetermining a shape element, the measurement results may considerablydeviate from the real geometry.

Conventional methods of filtering disturbing values from measurementsignals are based on smoothing of the measuring values. Examples aresmoothing by the moving average method, smoothing by low-pass filtering,and optimum filtering (Wiener filter). All these methods have a commondisadvantage in that the influence of so-called outliers, which aremeasured values largely differing from the actual values, though beingminimized by averaging, still impair the measuring result.

The accuracy of the measurement results is improved if the input values(in particular probed point coordinates) are separated from potentialoutliers before further metrological processing, such that only theremaining measurement values, which are free of outliers, are furtherprocessed. For that purpose, DE 199 00 737 A1 proposes that a firstregression calculation be followed by high-pass filtering to eliminatethose probed point coordinates that have to be regarded as disturbedvalues. The downside of this procedure is that the filtering ofdisturbed values is based on a previously calculated shape element thatis already affected by the disturbed input data. This may involveconsiderable deviations in the position and orientation of the shapeelement, such that the subsequent filtering process may not filter outthe actual disturbed values.

In principle, regression analysis is a method of fitting a geometricprimitive (shape element) to a non-ordered set of input coordinatepoints P_(i)(x_(i), y_(i)) with the aim to minimize the sum ofdeviations of the points P_(i)(x_(i), y_(i)) relative to the shapeelement. Known measures of deviation are the absolute value of thedistance, the square of the distance, and the amount of the distancebetween the maximum positive and maximum negative deviations relative tothe shape element. Regression analysis does not include a determinationas to whether the resulting shape element is the best possiblerepresentation of the aggregate of measurement points. However, if theset of input coordinate points P_(i)(x_(i), y₁) is representative of theshape element, the shape element is more precisely characterized than bythe recognition methods described below.

Apart from the analysis of coordinate measurements, the elimination ofdisturbing structures also plays an essential part in problems oftechnical recognition. In order to increase the certainty of informationin the detection of obstacles by a low-flying aircraft, DE 100 55 572 C1proposes that overhead power lines be detected in distance measuringimages by means of a detection method for horizontal straight lines,known to persons skilled in the art as Hough transformation. With thisprocedure, however, the derivation of detailed information about theposition and orientation of the shape element can only be achieved withconsiderable computational effort. For tasks in precision measurement,methods of regression analysis of shape elements may be used with muchgreater efficiency.

DE 695 23 965 T2 describes a recognition device and a correspondingmethod for a two-dimensional code, wherein a regression analysis iscarried out with all acquired image points. Further, a Houghtransformation is performed for the recognition of straight lines,wherein the number of points used in the method is reduced before thetransformation is made, in order to reduce the computational effort.

In general, the recognition of shape elements means a determination asto whether a desired shape element occurs within the given flat orthree-dimensional measuring volume. The identification process includesa determination of the best possible agreement between the shape elementand the arrangement of inputted coordinate points P_(i)(x_(i), y_(i))(aggregate of measurement points). Together with the identification, theidentified shape element is characterized by characteristic parameters(position, size, orientation). In recognition methods, only a coarsedetermination of position, size and orientation is possible due to thehigh computational effort.

The Hough transformation is a tool for recognizing geometric primitives,such as straight lines and circles, in a non-ordered set of inputcoordinate points P_(i)(x_(i), y_(i)).

Unlike in fitting geometric primitives by methods using regressionanalysis, where measures characterizing deviations are used, the Houghtransformation uses a frequency measure. The criterion for recognizingthe geometric primitive is: the maximum number of input coordinates lieon the geometric primitive. Therefore, the Hough transformation is veryrobust against disturbances having a low probability of occurrence, suchas outliers, contour breaks, or hidden features of the structure to beidentified.

The above mentioned Hough transformation is a recognition method and wasdeveloped for detecting straight-line features within a Cartesiancoordinate space. A fundamental illustration can be found, e.g., in U.S.Pat. No. 3,069,654. The potential applications of the Houghtransformation are also described in “Praxis der digitalenBildverarbeitung und Mustererkennung” by P. Haberäcker, Munich, Hanser,1995.

The invention provides a method in which the position and orientation ofthe wanted type of shape element in a greatly scattered aggregate ofinput coordinate points can be determined, while retaining theadvantages of the low measurement uncertainty of shape elementregression analysis.

The method according to the invention is based on an innovativecombination of recognition methods that are robust against measurementerrors and the more accurate shape element regression analysis for therespective same type of desired shape element. The decisive point isthat all relevant measurement results are supplied to a recognitionmethod for a shape element which is, as a general rule, predefined.After recognition of the shape element, the measurement values outside agiven range can be excluded as outliers. The subsequent regressionanalysis applied to the remaining measurement values results in a muchmore accurate determination of the shape element, since the outliers nolonger deteriorate the result of the determination of the shape elementin this process step.

Even though the following description of the method largely refers tothe example of optically acquired measurement values, it should be notedthat the method is generally applicable to measurement values acquiredby any suitable method. The optical acquisition of measurement datarepresents a preferred example since it may suffer from particularlyserious deviations from the actual structure due to soiling andillumination effects.

Preferably, the Hough transformation is used as a recognition methodperformed in the beginning. However, also other recognition methods maybe used, which need not be illustrated herein in more detail as they areknown to those skilled in the art.

A considerable improvement in the suppression of disturbances, ascompared to the prior art, is achieved in that the type of the desiredshape element is already taken into account when the input coordinatepoints are examined with respect to the presence of outliers. This is incontrast to the conventional method where the type of the desired shapeelement is taken into account only in the subsequent processing (in thenext processing step).

One advantage of the method according to the invention is that outlieridentification relates to the shape of the desired shape element. Thus,the determination of the position and orientation of a particular shapeelement is possible even from a highly scattered aggregate of points.This is in particular necessary in measuring methods using imageanalysis, if the structures to be recognized are disturbed by unknownsystematic structures in the foreground or the background of the image.Examples are the recognition of geodetic features in images of alandscape, or the determination of geometric features of edges ofobjects in the image that are contaminated by dust.

The use of robust recognition methods for determining the position andorientation of the shape element permits reliable filtering ofdisturbing structures and does also provide a starting solution for thesubsequent regression analysis of shape elements, which is not affectedby disturbing signals.

An advantageous embodiment is characterized by performing a preliminaryregression analysis of the shape element in advance of the Houghtransformation used as a recognition method. The generation of a qualityparameter (E) from the result of the shape element regression analysis,and the determination whether this parameter (E) lies within a giventolerance range (T) allows the user to decide whether the computationaleffort of the Hough transformation is required at all.

FIG. 1 illustrates a surveying apparatus according to an embodiment ofthe invention. The surveying apparatus is a video tacheometer 101comprising a tripod mounting structure 103 having a mounting base 105and three legs 107 for supporting the mounting base 105 on a ground. AU-shaped alidade 109 is mounted on the mounting base 105 to be rotatableabout a vertical axis 111 and supports a camera 113 to be rotatableabout a horizontal axis 115 relative to the U-shaped alidade 109. Thecamera 113 comprises an objective lens 119 and a semiconductor imagesensor (not shown in FIG. 1) contained in a housing 117 of the camera113. Image data detected by the camera are supplied to a controller 121of the video tacheometer 101. The controller 121 has a tablet-PC havinga display 123 and input devices such as buttons 125. The display 123 isa touch screen display such that the display also may function as aninput device of a controller 121.

The image data supplied from the camera 113 are displayed on the touchscreen display 123 to visualize an image of objects disposed in thefield of view of the camera 113. In the example illustrated in FIG. 1,the objects within a field of view of the camera 113 comprise a building127 having a wall 129, a window 131 and a roof 133. A further objectwithin the field of view of the camera 113 is a tree hiding a portion ofthe building 127. For the purpose of the illustration of the presentembodiment, it is assumed that it is a task of a user of the videotacheometer 101 to measure the position and orientation of an upper edge137 of the roof 133. For this purpose, an image processing softwarerunning on a processing unit of the controller 121 has to identify theedge 137 using the method illustrated in more detail herein below.

As a first step, the user selects a region of interest in the imagecontaining the edge 137 to be analyzed. The region of interest isselected by the user using a pen cooperating with the touch screendisplay. The region of interest is indicated on the display 123 by arectangle 139 shown in broken lines.

The user then selects a type of the geometric shape element which issuitable for approximating the desired feature of the object, which isthe edge 137 of the roof 133. In this present example, the suitable typeof the shape element will be the straight-line, and the user may performthe selection using the buttons 125 or touch screen display 123.

The image processing is then started by the user, for example byoperating one of the buttons 125.

One problem encountered in the image analysis is the fact that a portionof the edge. 137 is hidden by the tree 135, and conventional methods,such as regression analysis and fitting might not be able to determineparameters of the straight-line such that the straight-linesubstantially coincides with the edge 137 of the roof 133 in the image.

The image processing starts by filtering the image data representing theregion 139 of interest using a suitable method, for generating measuringdata representing contours of the objects within the region 139 ofinterest, such as a contour of the tree 135 and a contour of the roof133. The measuring data are then further processed as illustrated hereinbelow.

FIG. 2 is a graph showing an example of a set of measuring datagenerated from a selected region 139 of interest as illustrated above.The measuring data are represented as data values of probed pointsP_(i)(x, y) acquired during the measurement of an object, such as theroof 133 in FIG. 1, along a straight edge. As illustrated above, opticalprecision measurements are frequently impaired by the disturbances, suchas the test object being hidden by an obstacle during the measurement.In other applications, where a measurement is performed during amanufacturing process, soiling of the object may present a similarproblem. Even the slightest deposits remaining from the manufacturingprocess may lead to the test object being wrongly rejected. FIG. 2clearly illustrates the influence of disturbances by outliers among theprobed points within a range of x=0 to 0.5 mm.

To solve this problem, the invention uses a combination of recognitionand regression analysis for shape elements.

The graph shown in FIG. 3 includes a straight fitting line resultingfrom a Gaussian regression analysis for the desired shape element, ifthe points (measured data) are supplied to the regression analysiswithout prior filtering. FIG. 3 shows that the outliers among themeasured data have a considerable influence on the course of the fittingline, which deviates from the evidently positive slope of the aggregateof points in x direction and exhibits a negative slope instead.

To determine whether the calculated shape element is valid, a ParameterE is derived and compared with a predefinable tolerance range T. Theparameter E characterizes, for example, a range about the obtained shapeelement by way of the Euclidean distance (see also below with referenceto FIG. 6). The parameter E may be determined, e.g., by the shapedeviation f or the standard deviation s of the calculated shape element.It is apparent that other rules for determining E by means of regressionanalysis can be derived. Assigning a suitable rule for determining E tothe respective measurement job can be effected according to thefollowing conditions:

-   -   If all measured points are to lie within the tolerance range T,        then E=f is fulfilled.    -   If (assuming a Gaussian distribution of the deviations of the        inputted points from the calculated shape element) at least        99.73% of the inputted points are to lie within the tolerance        range T, then E=3 s is fulfilled.    -   If (assuming a Gaussian distribution of the deviations of the        inputted points from the calculated shape element) at least 95%        of the inputted points are to lie within the tolerance range T,        then E=2 s is fulfilled.

If the parameter E exceeds a permissible predefinable tolerance range T,the shape element determined by regression analysis cannot be regardedas valid. It is here that the invention comes in with a filtering of themeasured coordinate points.

FIG. 5 is a flow chart showing process steps of the method for analyzingthe measuring data. The method starts with step 1. In order to improvethe result of the shape element determination, a shape elementrecognition procedure is performed first. For this purpose, the bestposition of the shape element in the sense of its “most frequentoccurrence” is determined in step 2, the recognition method being, e.g.,a Hough transformation. The Hough transformation involves all coordinatepoints (probed points) P_(i) as far as they lie within a predefinablevalidity range that is independent of the position of the shape element.It is, of course, feasible to exclude particular measured data fromprocessing if they are obviously outside the validity range. The probedpoints P_(i) are defined by their coordinates (x, y, z) and may be,depending on the measurement job, two-dimensional or three-dimensionalvalues.

In addition, the type of the desired shape element can, in most cases,be determined by the specific measurement job. A variety of recognitionmethods are known to those skilled in the art, so that there is no needto illustrate the operation of these methods in detail here. Forillustration purposes, FIG. 7 shows a representation in the Houghparameter space, exemplified by the shape element “straight line”.

Analogously to the procedure described above for the determination ofshape elements by regression analysis, a range E about the shape elementFE determined by the Hough transformation is derived, wherein therelevant probed points Pr_(i) have to lie within that range. FIG. 6depicts such selection as a diagram in which the probed points P_(i)through P₈ are shown with the shape element “straight line” serving asan example. Points outside the range E, such as, e.g., P₅, areeliminated from the aggregate of points. With reference again to FIG. 5,the selection of the relevant points to remain in the aggregate is madein step 3. In the subsequent step 4, the remaining aggregate of pointsPr_(i) is subjected to a regression analysis. Thus, the regressionanalysis is performed with the outliers being excluded, wherein theoutliers are located outside the range E after the recognition step. Theregression analysis therefore achieves more precise results as comparedto a regression analysis performed on data including the outliers. Themethod terminates with step 5.

FIG. 4 is a diagram showing the aggregate of points shown in FIG. 2, andthe result calculated by the illustrated method, which is the linedetermined by regression analysis. The method may also include furtheriterative steps.

FIG. 8 is a flow chart of a further embodiment of the method. Ascompared to the embodiment of the method illustrated with reference toFIG. 5, a main difference is that the recognition (Hough transformation)performed in step 2 is preceded by an additional shape elementregression analysis. This preceding shape element adjustment calculationis made in step 6 and takes into account the inputted coordinate pointsP_(i). The determination of the desired shape element in the firstprocess step by means of shape element regression analysis increases thespeed of the method, provided that the input points are free fromdisturbances. The subsequent time-consuming Hough transformation is onlyperformed if the determination made in step 7 reveals that the qualityparameter E of the regression analysis is outside a predefinabletolerance range T. If E is within the permissible, predefinabletolerance range T, the shape element is declared valid in step 8 andissued as the measurement result.

In applications in which the desired shape element may not be locatedwithin the input data or in which several shape elements of the sametype exist, the Hough transformation is to be performed as the firstprocess step, as it is shown in FIG. 5. One advantage of this variantis, as compared to the variant according to FIG. 8, that the recognitionprocess can determine not only the position but also the type of themost frequently occurring shape element. Moreover, the preliminaryrecognition method can detect, in a single step, plural shape elementsof a same type which occur with a predefinable minimum frequency. Thismakes it possible to segment the input coordinates and to performsubsequent regression analyses in the various coordinate segments.

The types of shape elements that can be determined by the methodaccording to the invention generally include standard 2D and 3D shapeelements, space curves, and combined shape elements.

The software for operating the user interface of the controller 121 andperforming the image analysis and the analysis of the measuring data maybe loaded into the controller 121 by a suitable carrier comprising a CDROM inserted in a CD ROM drive (not shown in FIG. 1) of the controller121. Further, the software may be loaded into the computer from asuitable network, such as the Internet, through a network interface ofthe controller.

FIG. 9 is an illustration of a computer system according to anembodiment of the invention. The computer system 201 may be Included inthe controller 121 shown in FIG. 1, or the computer system 201 may beseparate from a surveying apparatus. The computer system 201 isconfigured to perform the methods of analyzing measuring data asillustrated above, and comprises a processor 203, a peripheral interface205 used by the processor to access peripheral devices, a mass storagedevice 207, such as a hard disc, for storing a bootable operatingsystem, program instructions of application programs and other data.Reference numeral 209 in FIG. 9 represents a memory area accessed by theprocessor 201. The memory area may be a virtual memory area includingportions located in a physical semiconductor memory and on the massstorage device 207. Peripheral devices connected to the interface 205comprise an input device 211 such as a keyboard, a mouse, other buttons,a touch screen display and others, a display 213, and a networkinterface 215.

Using the input device 211, the user may instruct the computer system201 to load measuring data generated, for example, by a surveyingapparatus, into the computer system 201 through the network interface215. The user may then instruct the computer system 201 to start aprogram for analyzing the measuring data, and the computer system 201will load program instructions which are executable by the processor 203to perform the analyzing method from the mass storage device 207 intothe memory area 209. Reference numeral 217 in FIG. 9 represents a groupof sectors of the mass storage device in which the program instructionsfor the analyzing program are stored, and reference numeral 219illustrates a portion of the memory area 209 in which the programinstructions are stored after loading the program instructions from themass storage device 207 into the memory area 209. The programinstructions 219 may be accessed by the processor 203 for executing theprogram to perform the analyzing methods as illustrated above. Resultsof the analysis may be displayed on the display device 213 and stored onthe mass storage device 207.

In the above illustrated embodiments, the method of analyzing measuringdata is applied to image data obtained from a surveying apparatus. Itshould be noted, however, that the analyzing methods and the computersystem 201 may be used for analyzing measuring data obtained by anypossible application. The measuring data may be obtained from ameasurement of any physical or other property. According to an example,the measuring data may be image data obtained in applications other thansurveying applications. For instance, the image data may represent animage of articles of manufacture. The analysis of the measuring data maybe used to determine a quality of the articles of manufacture, where theimage data are disturbed by electronic noise, soiling, artifacts orother influences.

To summarize, embodiments of the invention relate to a method forminimizing the influence of disturbing signals during calculation ofshape elements from coordinate points. An aim of the embodiments of theinvention is to exclude the coordinates which are not to be locallyassigned to the desired shaped element from the calculation of theshaped element. Said aim is achieved by combining compensation methodsfor calculating the desired type of shaped element with recognitionmethods for the same type of shaped element and using the recognitionmethods for filtering the coordinate points that are relevant forcalculating the shaped element out of all input coordinate points.

The present invention has been described by way of exemplary embodimentsto which it is not limited. Variations and modifications will occur tothose skilled in the art without departing from the scope of the presentinvention as recited in the appended claims and equivalents thereof.

The present application in particular discloses the following items (1)to (9):

-   -   (1) A method for minimizing the influence of disturbing signals        in calculating shape elements from input coordinate points,        comprising the following steps:        -   performing a recognition method involving all input            coordinate points for calculating a recognized shape            element;        -   filtering out relevant coordinate points from the input            coordinate points, which are located within a predefinable            parameter about the shape element determined in the            recognition method; and        -   performing a regression analysis for the same type of shape            element, involving the filtered out relevant coordinate            points, for computing a final shape element.    -   (2) The method according to item (1), characterized in that the        recognition method is preceded by a regression analysis of the        desired shape element involving all input coordinates, such that        performing the steps of performing the recognition method        involving all input coordinates, performing the filtering out of        the relevant coordinate points, and of performing the regression        analysis with the filtered-out coordinate points only occur if a        validity parameter of the shape element determined in the        preceding regression analysis is outside a predefinable        tolerance range.    -   (3) The method according to item (1) or (2), characterized in        that the recognition of the position and orientation of the        desired type of shape element within the disturbed input        coordinate points is performed by means of the Hough        transformation and the determination of singular points in the        Hough parameter space.    -   (4) The method according to one of items (1) to (3),        characterized in that, a multidimensional range about the        determined shape element is determined for the purpose of        deciding whether the shape element calculated in the preceding        adjustment calculation is valid and/or for the purpose of        filtering out the relevant coordinate points by means of the        recognition procedure, wherein the region is defined by the        Euclidean distance from the shape element.    -   (5) The method according to item (4), characterized in that the        Euclidean distance is determined on the basis of a specified        tolerance range of the shape deviation of the shape element.    -   (6) The method according to item (4), characterized in that the        Euclidean distance is determined on the basis of the statistical        distribution of the residual deviation from the desired type of        shape element.    -   (7) The method according to item (6), characterized in that the        standard deviation is determined from the statistical        distribution of the residual deviation from the desired type of        shape element, and that the standard deviation is taken into        account in the determination of the distance.    -   (8) The method according to one of items (1) to (7),        characterized in that the recognition procedure identifies the        type of shape element.    -   (9) The method according to item (8), characterized in that the        recognition procedure determines plural shape elements of the        same type in one process step, and that the subsequent filtering        out of the relevant coordinate points and the shape element        regression analysis are applied to all shape elements found in        the recognition procedure.

1. A computer-readable medium comprising: computer-readable informationrepresenting parameters which represent at least a portion of a featureof an object as a geometric shape, the parameters determined by acomputer executing the steps of: (a) a recognition method performed on aplurality of data values to determine parameters representing at leastthe portion of the feature of the object as an initial geometric shape,(b) eliminating data values having a distance from the initial geometricshape represented by the determined parameters greater than a firstthreshold, or (c) performing a regression analysis on the remaining datavalues to determine the parameters of the computer-readable information.2. The computer-readable medium of claim 1 further comprising computermemory.
 3. The computer-readable medium of claim 2, wherein the computermemory comprises a mass storage device.
 4. The computer-readable mediumof claim 1, wherein the computer-readable medium is loadable from anetwork.
 5. The computer-readable medium of claim 4, wherein the networkis a wireless network.
 6. A method of analyzing measuring datacomprising the steps of: obtaining measuring data from a measuringdevice, the measuring data comprising data values representing at leasta portion of a feature of an object; performing a recognition method onat least a subset of the data values to determine parametersrepresenting at least the portion of the feature of the object as aninitial geometric shape element; eliminating data values having adistance from the initial geometric shape element represented by thedetermined parameters greater than a first threshold; and performing aregression analysis on at least a subset of data values not soeliminated to determine parameters of a geometric shape element betterrepresenting at least the portion of the feature of the object.
 7. Themethod of claim 6, further comprising the step of transmittinginformation representing at least a portion of the measuring data to acomputing device remote from the measuring device.
 8. The method ofclaim 6, further comprising the step of transmitting informationrepresenting at least a portion of the subset of the data values to acomputing device remote from the measuring device.
 9. The method ofclaim 6, further comprising the step of transmitting informationrepresenting at least a portion of the subset of data values not soeliminated to a computing device remote from the measuring device. 10.The method of claim 6, further comprising the step of transmittinginformation representing one or more of the parameters of the geometricshape element which better represent at least the portion of the featureof the object to a computing device remote from the measuring device.11. A computerstorage medium containing information representing acomputer program adapted to cause a processing unit to execute themethod of claim 6.